In recent years, plastic optical fiber (POF) has been considered as a promising costeffective scheme for short-distance data communications, multimedia communication in cars, and in-house networks. However, due to the intrinsic nature of the relatively large numerical aperture of POF and the high attenuation rate, implementing high data rates over 100-m POF transmission length will be a significant challenge. We propose a scheme of high-speed 100-m POF transmission system based on a visible red laser and a cascaded neural network (NN) post-equalizer. To mitigate the nonlinear distortion induced by the POF, three different NNs, i.e., convolutional NN (CNN), long and short-term memory NN (LSTM), and cascaded NN structure consisting of convolutional layers and LSTM (CNN-LSTM), are employed as the post-equalizer. Experimental results show that using three different post-equalizers can significantly improve the system performance compared with the Volterra equalizer baseline. Among them, CNN-LSTM can outperform the others in terms of the bit error rate (BER) and the system Q-factor in the low nonlinear region. When the system operating in strong nonlinear region, CNN can achieve optimal performance at a lower system overhead of complexity. Finally, we successfully demonstrated a 100-m POF transmission system using 16 quadrature amplitude modulation discrete Fourier transform-spread orthogonal frequency division multiplexing modulation format at 1.8 Gbps with BER below 3.8 × 10 −3 by utilizing CNN-LSTM.
Edge computing perfectly integrates cloud computing centers and edge-end devices together, but there are not many related researches on how the edge-end node devices work to form an edge network and what the protocols used to implement the communication among nodes in the edge network. Aiming at the problem of coordinated communication among edge nodes in the current edge computing network architecture, this paper proposes an edge network routing and forwarding protocol based on target tracking scenarios. This protocol can meet the dynamic changes of node locations, and the elastic expansion of node scale. Individual node failures will not affect the overall network, and the network ensures efficient real-time with less communication overhead. The experimental results display that the protocol can effectively reduce the communications volume of the edge network, improve the overall efficiency of the network, and set the optimal sampling period, so as to ensure that the network delay is minimized.
As 6G research progresses, both visible light communication (VLC) and artificial intelligence (AI) become important components, which makes them appear to converge. Neural networks (NN) as equalizers are gradually occupying an increasingly important position in the research of the physical layer of VLC, especially in nonlinear compensation. In this paper, we will propose three categories of neural network equalizers, including input data reconfiguration NN, network reconfiguration NN and loss function reconfiguration NN. We give the definitions of these three neural networks and their applications in VLC systems. This work allows the reader to have a clearer understanding and future trends of neural networks in visible light communication, especially in terms of equalizers.
Recently, visible light communication (VLC) has emerged as a promising communication method in 6G. To achieve 6G high-speed transmission, wavelength division multiplexing (WDM) based VLC systems are a highly promising candidate. However, the “yellow and green gap” greatly limits the yellow light efficiency of InGaN-based LEDs and also restricts the transmission rate of yellow LEDs. In addition, pre-equalization and post-equalization also have an important impact on high-speed communication. In this paper, we propose to employ a vertical InGaN-based Si-substrate yellow LED with bit-power loading discrete multitone (DMT) modulation and a novel cascaded pre-equalizer network to achieve a high-speed yellow-light VLC system. The proposed cascaded pre-equalizer network is based on a digital Zobel network and a partial nonlinear pre-equalizer (DZNPN). The microscopic time-domain transient response of the high-speed and large-amplitude signal is also investigated to show a severe impairment. Utilizing the DZNPN cascaded pre-equalizer network based on the third-order Volterra series, a record-breaking data rate of 3.764Gbps over 1.2 m free space and 3.808Gbps over 0.7 m are experimentally demonstrated under the hard decision-forward error correction (HD-FEC) threshold of 3.8 × 10−3. The rate can be improved from 2.818Gbps to 3.764Gbps with 650Mbaud compared to the un-preprocessed signal. This is the highest data rate ever reported for yellow-light VLC systems based on a single LED to the best of our knowledge.
Video summarization aims to automatically generate a diverse and concise summary which is useful in large-scale video processing. Most of the methods tend to adopt self-attention mechanism across video frames, which fails to model the diversity of video frames. To alleviate this problem, we revisit the pairwise similarity measurement in self-attention mechanism and find that the existing inner-product affinity leads to discriminative features rather than diversified features. In light of this phenomenon, we propose global diverse attention which uses the squared Euclidean distance instead to compute the affinities. Moreover, we model the local contextual information by novel local contextual attention to remove the redundancy in the video. By combining these two attention mechanisms, a video SUMmarization model with Diversified Contextual Attention scheme is developed, namely SUM-DCA. Extensive experiments are conducted on benchmark data sets to verify the effectiveness and the superiority of SUM-DCA in terms of F-score and rank-based evaluation without any bells and whistles.
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